How to Trade with ChatGPT. Tips and Tricks of AI Trading

It can make your trading faster and more efficient, but it can also make mistakes that would cost you if you don't double-check.

AI chatbots in real-life trading

AI chatbots don’t make their own trading decisions or predict the future. They can make your work easier and save you a lot of time by processing huge amounts of information and presenting it in a way that’s easy to understand.

Pattern recognition

ChatGPT can go through large chunks of historical market data and point out repeating patterns or price behaviors. It can help you spot setups you might not have noticed before.

Risk management

AI chatbots can help you think through your risk management. For instance, they can point out what might increase your exposure or suggest general ideas for position sizing and stop-loss placement. But at the end of the day, you’re the one who decides how to apply them when trading live.

News and sentiment analysis

Chatbots can scan volumes of headlines, reports, or social media to process the general mood in the market and identify topics of interest. That can help you stay aware of what’s happening and driving attention. You must absolutely remember that ChatGPT's responses are based on text patterns and past data, not real-time news or price feeds.

Portfolio organization

AI tools can help you organize your trades, track results, and gauge how diversified your portfolio is. They’re good at keeping things tidy and easy to review, but they don’t manage or optimize your investments for you.

Trading automation support

ChatGPT can help you process data and come up with trading ideas, but it can’t place or manage trades. Some other AI tools and trading bots can automate certain steps when linked to trading platforms, but they still need to be set up and watched by the trader.

Practical prompt examples for trading

Here are some ready-to-use prompts you can copy, paste, and adapt to make the most of ChatGPT while trading in stocks and Forex:

1. Market analysis

• “Summarize the latest macroeconomic factors driving gold and USD.”

• “Which sectors are outperforming in the US stock market this week?”

2. Technical analysis

• “Give me a technical breakdown of EURUSD on the 4-hour chart, including support, resistance, and trend direction.”

• “Explain common chart patterns forming on Bitcoin right now.”

3. Fundamental analysis

• “Summarize Apple’s financial performance from its latest earnings report.”

• “Compare the growth potential of Tesla vs. BYD for the next 5 years.”

4. Risk management

• “If I have a $10 000 account and can risk 2% on a trade with a 50-pip stop, what should my position size be on EURUSD?”

• “If my stop-loss is $5 below entry on gold, what profit target do I need for a 1:3 risk/reward ratio?”

• “Create a risk-reward analysis for a long trade on GBPJPY with a stop at 1.2700 and target at 1.3000.”

5. Coding and automation

• “Write an MQL5 script for MetaTrader that sends an alert when RSI crosses 70 on the 1-hour chart.”

• “Create Python code to pull historical S&P 500 data and calculate moving averages.”

These examples give you quick answers and calculations while also encouraging safer trading practices.

Using AI chatbots for trading: a step-by-step guide

1. Choosing the right chatbot

Key factors to consider:

• Functionality. Depending on what your trading strategy entails, you’ll need specific functions: real-time market data, automated trading, custom alerts, or what have you. Your chosen chatbot should feature those functions.

• Ease of use. When you’re trading, the last thing you want to do is get bogged down by a bad interface. Your chatbot should be a breeze to use and give you everything you need quickly.

• Customization. You’ve got to be able to set your chatbot to the specifications particular to your strategy and style.

• Integration. You need to be able to execute trades right from the chatbot, so it’s crucial that it can be integrated with whatever trading platform or exchange you’re using.

• Reliability. If your chatbot lags, that defeats the whole point. It needs to handle high volumes of traffic and data without lag or interruption.

Tools for AI-assisted trading

AI tools for trading fall into several main categories:

1. Large language models (LLMs)
ChatGPT, Claude, Grok

• Strength: Generating strategies, explanations, and prompts.

• Limitation: They don’t have live market data and can’t place trades automatically.

2. Coding helpers
ChatGPT (code mode), GitHub Copilot, Grok, MQL5/Pine Script communities.

• Strength: Turning ideas into code for MetaTrader 5 or TradingView.

• Example: “Write an MQL5 EA that buys when the 50 EMA crosses above the 200 EMA.”

3. Execution bots and integrations
MetaTrader 5 Expert Advisors, cTrader Automate, broker APIs.

• Strength: They can actually place and manage trades.

• Usually built manually or with AI-assisted code.

4. Popular chatbot platforms
These focus on automation and customization, often for crypto, but the same approach can apply to other markets as well:

• TradeSanta: Specifically designed for automated crypto trading, with strategy customization.

• 3Commas: Offers automated trading, custom alerts, and a wide range of indicators.

• Haasbot: Supports backtesting on historical data and custom strategy design.

• Zignaly: Combines automation, custom alerts, and multiple exchange integrations.

2. Training the chatbot to your trading needs

You’ll have to explain your trading style, your preferences, your risk tolerance, and your investment goals to your chatbot. You’ll have to continue honing its ‘understanding’ of what you want from it as you go.

3. Setting up the chatbot for trading

Once your trading chatbot is trained, you can connect it to your trading account and configure it so it receives whatever data it needs to.

4. Interacting with the chatbot and receiving trading insights

Ask your chatbot questions. It can give you real-time market data, analysis, and recommendations based on your preferences and trading history.

5. Test and validate your strategy

If you use ChatGPT to create trading rules or code, make sure you test them. Load the script into MetaTrader 5 (MQL5) or TradingView (Pine Script) and run it on historical data first to see how it would have performed in the past. That’s called backtesting.

Once you’ve done that, you can proceed with out-of-sample testing and walk-forward validation to see how it performs with new data, not just the kind it was built on.

Risks and limitations of using AI chatbots in trading

Over-reliance on technology

One of the biggest risks of using AI chatbots in trading is over-reliance on the technology.

Technical issues and glitches

Another risk of using AI chatbots in trading is technical issues and glitches that can impact their accuracy and reliability.

Limited availability of reliable data

Chatbots rely on data to generate insights and recommendations, and the availability of reliable data can be a challenge in some markets.

Privacy and security concerns

Finally, privacy and security concerns are associated with using AI chatbots in trading. Traders should ensure their trading accounts and personal information are secure.

Regulatory and compliance reminders:

• Check your broker’s rules on automation before connecting a bot or script.

• Only used approved data sources and APIs from your broker or exchange.

• Follow local laws and licensing requirements that apply to your trading activity.

• Review and respect the platform's terms of service before running automated strategies.

Security and data protection checklist:

– Use read-only API keys.

– Keep demo and live accounts separate to avoid mistakes.

– Don’t paste personal or financial data into chats.

– Enable 2FA and revoke unused permissions.

How do FBS analysts use AI chatbots?

FBS analysts, just like FBS clients, are developing daily and trying to improve their trading skills and results. Here are three insights that can make a real difference as you trade.

1. Create a trading strategy

Let's assume you have two indicators you prefer to use but need to know how to combine them into a trading strategy. ChatGPT can solve this problem.

Here is a dialogue:

FBS analyst: Hi, chat! Please give an example of a trading strategy combining the 200-period MA and the RSI.

ChatGPT: Certainly, here's an example of a trading strategy that combines the 200-period moving average (MA) and the Relative Strength Index (RSI):

Identify the trend using the 200-period MA. The trend is bullish if the price is above the 200-period MA. The trend is considered bearish if the price is below the 200-period MA.

Use the RSI to confirm overbought or oversold conditions. An RSI reading above 70 indicates an overbought condition, while an RSI reading below 30 indicates an oversold condition.

Look for a bullish signal when the trend is bullish, and the RSI is oversold. A bullish signal is generated when the price crosses the 200-period MA and the RSI is below 30, indicating a potential trend reversal.

Look for a bearish signal when the trend is bearish, and the RSI is overbought. A bearish signal is generated when the price crosses below the 200-period MA and the RSI is above 70, indicating a potential trend reversal.

Use stop-loss orders to limit losses if the trade goes against you.

Use profit targets to take profits when the trade goes in your favor.

FBS analyst: Where should I put my stop loss using this strategy?

ChatGPT: The placement of your stop loss using this strategy will depend on your risk tolerance, market volatility, and trading goals. Generally, a stop loss is placed below the recent swing low in a bullish trade and above the recent swing high in a bearish trade. This helps limit losses if the market moves against your position.

In the case of this strategy, if you are going long after a bullish signal is generated, you can place a stop loss below the recent swing low or below the 200-period MA, whichever is lower. If you go short after a bearish signal is generated, you can place a stop loss above the recent swing high or above the 200-period MA, whichever is higher.

Isn't it amazing?

2. Create a trading bot

After creating and back-testing the trading strategy, we want to automate it and create our first trading bot. You can create trading bots for MT5 using the MQL5 language. Describe your strategy as clearly as possible and pick the programming language. In our example, we will use the strategy from the previous paragraph.

After ChatGPT built a code for our bot, testing it before using it on a real account is essential. However, isn’t it a great tool to dive into the EA niche without programming knowledge?

3. Tests → results → refinement

You might notice it does well when the market is trending but struggles when prices move sideways. Or maybe it performs great during high-volume sessions but gives too many false signals when the market gets quiet. Take note of your observations. You can take those insights back to ChatGPT and ask it how to adjust the settings or rules so the strategy handles those situations better. For example:

“How can I adjust the RSI thresholds to reduce false signals in ranging conditions?”

By repeating this loop - prompts, coding, testing, and reviewing — you gradually build stronger strategies and gain practical experience.

4. Ask anything

For example, what time should you trade XAUUSD? Ask the bot, and you will get a great answer:

Note that the answer can be even more accurate if you include your trading style, for example. Go ahead, try it, and share your results in the comments!

Glossary of AI trading terms

LLM (Large Language Model): The AI system behind ChatGPT. It generates text, ideas, and code, but it doesn’t connect to live markets or take real-life action on your behalf.

API (Application Programming Interface): A way for one program to connect with another. Traders use APIs to link bots or scripts with broker platforms.

Backtest: Running a strategy on past data to see how it would have performed. It’s an important step before trying anything with real money.

Overfitting: When a strategy looks perfect on old charts because it’s tailor-fitted to them, but then fails in live trading.
RR (Risk/Reward ratio): A way to compare possible loss with possible gain. A 1:2 ratio means risking $1 to try to make $2.